Multi-sense environmental monitoring device and method

ABSTRACT

An environmental monitoring device for detecting and warning users of unhealthy levels of a given substance is disclosed having more than one sensor for each substance to be detected. Each sensor for each substance detected may be positioned in more than one plane or surface on the device. The device may be capable of auto or self calibration. Methods for reading substance levels and auto calibrating are also disclosed.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. application Ser. No.13/168,577 entitled “Multi-Sense Environmental Monitoring Device andMethod,” filed Jun. 24, 2011, which claims benefit of priority to U.S.Provisional Patent Application No. 61/358,729 filed on Jun. 25, 2010entitled “Multi-Sense Environmental Monitoring Device and Method,” theentire contents of which are hereby incorporated by reference in theirentireties.

FIELD OF THE INVENTION

Embodiments of the present invention generally relate to environmentalmonitoring devices.

BACKGROUND OF THE INVENTION

In a number of industrial work environments workers are at risk of beingexposed to a variety of hazardous environmental substances such as toxicor highly combustible gases, oxygen depleted environments, or radiation,etc. that pose a serious threat to worker safety. In order to keepworkers safe, specialized environmental monitoring devices are used toalert workers of dangerous changes in their immediate environment.

Current practice involves using fixed point monitoring devices thatmonitor the environment around where they are deployed or portablemonitoring devices that are carried by the workers to monitor theirimmediate vicinity. Fixed point monitoring devices are typically usedaround potential hazard locations such as confined spaces to warnworkers of the environment before they enter. Portable monitoringdevices are often used for personal protection. These monitoring devicesmay have a single sensor to monitor one specific substance or multiplesensors (typically two to six) each monitoring a distinct substance.

Given that these environmental monitoring devices are life critical, itis important the device functions properly and accurately. Currentpractice involves periodic bump testing and calibration of monitoringdevices to guarantee proper functioning. Bump tests involve exposing themonitoring device to a measured quantity of gas and verifying that thedevice responds as designed, i.e., it senses the gas and goes intoalarm. Calibration involves exposing the device to a measured quantityof gas and adjusting the gain of the sensors so it reads the quantity ofgas accurately. The purpose of calibration is to maintain the accuracyof the monitoring device over time.

Current best practice followed by leading manufacturers of environmentalmonitors recommends bump testing the monitoring device before every dayswork and calibrating the device once at least every thirty days. While anumber of manufacturers sell automated docking stations thatautomatically perform calibration and bump testing when a monitoringdevice is docked, there are still a number of disadvantages to thecurrent practice.

A fixed bump and calibration policy, such as currently practiced, doesnot take into account the actual state of the sensors or theenvironmental monitoring device. Such a fixed policy (bump test everyday and calibrate every thirty days) by its very nature is a compromisethat is too stringent in many cases and too liberal in many others.

Given that the docking operation requires the user to bring the monitorto a central location, which typically is outside the work area, toperform the bump test and calibration, there is value inminimizing/optimizing this operation as much as possible withoutcompromising safety.

Threshold limit values (TLV), namely the maximum exposure of a hazardoussubstance repeatedly over time which causes no adverse health effects inmost people is constantly being reduced by regulatory authorities asscientific understanding and evidence grows and we accumulate moreexperience. Often these reductions are quite dramatic as in the case ofthe recent (February 2010) reduction recommended by the AmericanCongress of Governmental Industrial Hygienists (ACGIH) for H2S exposure.The ACGIH reduced the TLV for H2S from a time weighted average (TWA) of10 ppm to 1 ppm TWA averaged over eight hours. The effect of suchreductions puts a premium on accuracy of measurements. Current practiceof a fixed calibration policy, such as calibrate every thirty days, maynot be enough to guarantee the level of accuracy to meet the morestringent emerging TLV's. While a blanket reduction in the frequency ofthe calibration interval, i.e., from thirty days, will help to improveaccuracy, it would add significant cost to the use and maintenance ofthe environmental monitoring devices.

One solution to this problem, pursued by some, is to use newer and moreadvanced technology sensors with a higher degree of accuracy andtolerance to drift that minimize the need for calibration and bumptesting. While there certainly is value in this approach, the cost ofthese emerging sensor often preclude its widespread use, particularly inpersonal monitoring applications where a large number of these monitorsneed to be deployed.

For all the aforementioned reasons there is value in developing monitorsthat use current low cost sensor technologies while still meetingemerging TLV regulations and allow for a more adaptive calibration/bumppolicy that takes into account the state of the sensors and monitoringdevices.

SUMMARY OF THE INVENTION

In one general aspect, embodiments of the present invention generallypertain to a monitoring device having at least two sensors for eachsubstance to be detected, a display, a processing unit, and an alarm.The sensors may be positioned on more than one plane or surface of thedevice. The processing unit may auto or self calibrate the sensors.Another embodiment relates to a network of monitoring devices. Otherembodiments pertain to methods of monitoring a substance with amonitoring device having at least two sensors for that substance andauto or self calibrating the sensors.

Those and other details, objects, and advantages of the presentinvention will become better understood or apparent from the followingdescription and drawings showing embodiments thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings illustrate examples of embodiments of theinvention. In such drawings:

FIGS. 1A, 1B and 1C illustrate monitoring devices having two sensorsthat detect the same substance and positioned on different planes orsurfaces of the device, and FIG. 1D shows a monitoring device havingthree sensors according to various embodiments of the present invention;

FIG. 2 shows a block diagram illustrating a few of the components of themonitoring device according to various embodiments of the presentinvention;

FIG. 3 illustrates a flowchart of an example AI logic according tovarious embodiments of the present invention; and

FIG. 4A illustrates a monitoring device with the plurality of sensorshoused in multiple housings and connected to a central processing unitand FIG. 4B illustrates a network of monitoring devices according tovarious embodiments of the present invention.

DETAILED DESCRIPTION

Various embodiments of the present invention pertain to a monitoringdevice and methods used for environmental monitoring of substances, suchas, for example and without limitation, gases, liquids, nuclearradiation, etc.

In an embodiment, as illustrated in FIGS. 1A-C, the monitoring device 90has at least two sensors, 200 a and 200 b, which detect the samesubstance. The sensors may be positioned in more than one plane orsurface of the device 90. The device 90 also has a display 202; a userinterface 102, such as, for example and without limitation, at least onekey or key pad, button, or touch screen, for control and data entry; analarm 203, shown in FIGS. 1C and 1D, such as, for example and withoutlimitation, audio, visual, or vibration; and a housing 104. Themonitoring device 90 may have a user panic button 106, shown in FIGS. 1Aand 1B, that allows the user to trigger an alarm mechanism. In anexample, as shown in FIGS. 1A and 1B, sensor 200 a and 200 b are onopposite sides of the device 90. In another example, as shown in FIG.1C, sensor 200 a is on the front of the device 90 and sensor 200 b onthe top. In yet another example, as shown in FIG. 1D, the device 90 hasthree sensors, 200 a-c, sensing the same substance and positioned indifferent planes or surfaces of the device 90. The position of thesensors 200 in different and multiple planes greatly reduces thelikelihood of more than one sensor failing, for example by being cloggedby debris from the device 90 being dropped. The monitoring device 90 mayhave more than one sensor 200 for each substance to be detected, i.e.,the device 90 may detect more than one substance. The sensors 200 foreach substance may be positioned on more than one plane or surface ofthe device 90. For example, the device 90 may have two sensors 200 a and200 b for H2S positioned on different surfaces or planes, e.g., one onthe top and one on the side, of the device 90 and two sensors 200 c and200 d for oxygen positioned on different surfaces or planes of thedevice 90, e.g., one on top and one on the side.

In another embodiment the monitoring device 90, as shown in FIG. 2, hasa plurality of sensors 200 a-n that detect the same substance. Onebenefit of using more than one sensor 200 for each substance to bedetected is reduction in the frequency of bump testing and calibrationof the monitoring devices. As an example, in practice monitoring devicetypes typically used for gas detection have been found to fail at a rateof 0.3% a day based on field analysis data and thus daily bump testshave been mandated; however, equivalent safety may be gained with twosensors by bump testing every week, thereby reducing bump testing byseven fold.

In further embodiments, the monitoring device 90, as shown in FIG. 2,has a processing unit 201; a plurality of sensors 200 a-n that sense thesame substance, such as, for example and without limitation, a gas; adisplay 202; an alarm 203 that would generate an alarm, for example andwithout limitation, an audio, visual, and/or vibratory alarm; and amemory 204 to store, for example and without limitation, historic sensorand calibration/bump test data. The processing unit 201 interfaces withthe sensors 200 a-n and determines the actual reading to be displayed.The actual reading may be, for example and without limitation, themaximum, minimum, arithmetic, mean, median, or mode of the sensor 200a-n readings. The actual reading may be based on artificial intelligence(AI) logic. The AI logic mechanism takes into account, for example andwithout limitation, the readings from the plurality of sensors 200 a-n,historic sensor performance data in the memory 204, span reserve of thesensor 200, gain of the sensor 200, temperature, etc., to determine theactual reading. In another example, as an alternative to the displayedactual reading being the maximum of the aggregate of the n sensors 200a-n, the displayed actual reading may be calculated as follows, where Rdenotes the displayed reading and R_(i) denotes the reading sensed bysensor i:

$R = {\sqrt[k]{\frac{\sum\limits_{i = 0}^{n}R_{i}^{k}}{n}}.}$

Then, the processing unit may display possible actions that need to betaken based on the actual reading derived, for example and withoutlimitation, activate the alarm, request calibration by user, indicate onthe display that the sensors are not functioning properly, indicate thecurrent reading of gas or other substance in the environment, autocalibrate sensors that are out of calibration, etc.

One example of the artificial intelligence logic method would be for thegreater readings of the two sensors 200 a and 200 b or the greaterreadings of a multitude of sensors 200 a-n to be compared with athreshold amount, and if the sensor reading crosses the thresholdamount, an alarm mechanism would be generated. Another example of AIlogic entails biasing the comparison between the sensor readings and thethreshold amount by weights that are assigned based on the currentreliability of the sensors 200 a-n, i.e., a weighted average. Theseweights can be learned, for example and without limitation, fromhistoric calibration and bump test performance. Standard machinelearning, AI, and statistical techniques can be used for the learningpurposes. As an example, reliability of the sensor 200 may be gaugedfrom the span reserve or alternatively the gain of the sensor 200. Thehigher the gain or lower the span reserve, then the sensor 200 may bedeemed less reliable. Weights may be assigned appropriately to bias theaggregate substance concentration reading (or displayed reading) towardsthe more reliable sensors 200 a-n. Consider R to denote the displayedreading, R_(i) to denote the reading sensed by sensor I, and w_(i) todenote the weight associated by sensor i:

$R = \frac{\sum\limits_{i = 1}^{n}{w_{i}*R_{i}}}{n}$

where the weight w_(i) (0<w>1) is proportional to span reading of sensori or inversely proportional to the gain G. Alternatively, w_(i) can bederived from historical data analysis of the relationship between thegain w_(i) and span reserve or gain G_(i). Historical data of bump testsand calibration tests performed in the field, for example and withoutlimitation, can be used to derive this data.

In addition, as illustrated in FIG. 3, if the difference in readingsbetween any two or more sensors 200 is greater than some threshold valuet_(c), which could be determined in absolute terms or relativepercentage terms and may vary by substance, then the monitoring device90 would generate an alarm or visual indication in the display 202requesting a calibration by docking on a docking station or manually beperformed on the device 90. Further, if the difference in readings isgreater than some higher threshold value t_(f), the monitoring device190 would generate an alarm and or indicate on the display 202 a messageindicating a sensor failure.

In some circumstances, for example and without limitation, in the caseof an oxygen sensor, the minimum reading of a multitude of sensors 200a-n may be used to trigger an alarm to indicate a deficient environment.

In another embodiment, the monitoring device 90 may have an orientationsensor, such as, for example and without limitation, an accelerometer,that would allow the artificial intelligence logic to factor in relativesensor orientation to account for the fact that heavier than air gases,for example, would affect sensors in a lower position more than on ahigher position and lighter than air sensors would. The degree ofadjustment to the reading based on orientation can be learned, forexample and without limitation, from the calibration data, fieldtesting, distance between sensors, etc. and used to adjust readings frommultiple positions on the device 90 to give the most accurate reading atthe desired location, such as the breathing area of a user or a specificlocation in a defined space using the environmental monitoring device 90as a personnel protection device.

Another embodiment pertains to a network 500 having the plurality ofsensors 200 a-n that detect a single substance housed in separateenclosures, placed in the vicinity of one another, e.g., from inches tofeet depending on the area to be monitored, and communicate with oneanother directly and/or the central processing unit through a wirelessor wired connection. See FIGS. 4A and 4B. Each of the housings 104 mayhave a separate processing unit 201, memory 204, and AI processinglogic, as shown in FIG. 4B. Alternatively, or in combination, sensorunits would share a central processing unit 201 and memory 204, as shownin FIG. 4A.

Based on the plurality of sensor readings 200 a-n, the processing unit,using standard AI and machine learning techniques, etc., will adjust thegain of the sensors 200 a-n to match closer to the majority of sensors200 a-n for each substance, i.e., minimize variance among the sensors.The variance may be, for example and without limitation, a statisticalvariance, other variance metrics such as Euclidean distance, orcalculated from the average, weighted average, mean, median, etc.readings of the sensors. This would allow auto or self calibration ofoutlying sensors 200 a-n without the use of calibration gas using amanual method or a docking station. In an example, if n sensors 200 a-nsensing a particular gas, such as H2S, are considered and R_(i) is thereading that represents the concentration of H2S sensed by sensor i andM is the median value of the reading among the n sensors, then the gain,given by G_(i), of each sensor can be adjusted so that the reading R_(i)moves towards the median value by a small amount given by weightw(0<w>1). For each sensor i in (1,n):

$G_{i} = {G_{i}*\left( {w*\frac{R_{i}}{M}} \right)^{G_{i} = {G_{i}*{({w*\frac{R_{i}}{M}})}}}}$

Performing such gain adjustment whenever the monitoring device 90 isexposed to a substance in the field, for example, as part of day-to-dayoperation will reduce the frequency of calibrations required, thussaving money both directly from the reduction in calibrationconsumption, such as gas, and also costs involved in taking time away toperform the calibration. Current monitoring devices that use a singlegas sensor for detecting each gas type require a more frequentcalibration schedule, thereby incurring significant costs.

While presently preferred embodiments of the invention have been shownand described, it is to be understood that the detailed embodiments andFigures are presented for elucidation and not limitation. The inventionmay be otherwise varied, modified or changed within the scope of theinvention as defined in the appended claims.

EXAMPLE

The following discussion illustrates a non-limiting example ofembodiments of the present invention.

A single gas monitor that is used as a small portable device worn on theperson and used primarily as personal protection equipment may be usedto detect the gases within the breathing zone of the bearer of thedevice. The gas monitor is designed to monitor one of the followinggases:

Gas Symbol Range Increments Measuring Carbon Monoxide CO 0-1,500   1 ppmRanges: Hydrogen Sulfide H₂S 0-500 ppm 0.1 ppm Oxygen O₂ 0-30% of volume0.1% Nitrogen Dioxide NO₂ 0-150 ppm 0.1 ppm Sulfur Dioxide SO₂ 0-150 ppm0.1 ppm

The sensors are placed on two separate planes of the monitoring device,for example as depicted in FIGS. 1A-C. The gas concentration of thereading is calculated in the following manner:

${reading} = \frac{\sqrt{{{SensorReading}\; 1^{5}} + {{SensorReading}\; 2^{5}}}}{2}$

If the reading is higher (or lower in the case of oxygen) than a userdefined alarm threshold, then an audio and visual alarm is generated.

Further, if reading>0.5*abs(alarmThreshold−normalReading) and if

$0.3 \leq \frac{{abs}\left( {{{sensorReading}\; 1} - {{sensorReading}\; 2}} \right)}{\max \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)} \leq 0.5$

then an auto calibrate function based on gain as described below isperformed. The auto calibration may be done, based on a user definedsetting in the monitoring device, without further input from the user ofthe monitoring device, and/or the user will be informed that the gasmonitor has detected an anomaly and requests permission to autocalibrate.

If

$\frac{{abs}\left( {{{sensorReading}\; 1} - {{sensorReading}\; 2}} \right)}{\max \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)} \leq 0.5$

then a message is displayed to the user to calibrate the gas monitorimmediately using a calibration gas.

Gain of each of the sensors is modified as follows in the auto or selfcalibration process:

${sensorGain}^{new} = {{sensorGain}^{old} + {0.1*\frac{\max \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)}{\min \left( {{{sensorReading}\; 1},{{SensorReading}\; 2}} \right)}}}$

What is claimed is:
 1. A monitoring device for monitoring substances,the monitoring device comprising: a plurality of sensors, each of the atleast two sensors being configured to detect a same substance separatelyfrom all other sensors of the monitoring device and to generate anoutput signal in response to a detection of the same substance; aprocessing unit operably coupled to the at least two sensors, theprocessing unit being configured to: receive each of the output signalsfrom the at least two sensors, determine a detection signal for the samesubstance based on the output signals, and generate a calibration actionresponsive to at least two of the output signals deviating by athreshold amount, the calibration action comprising at least one ofperforming self-calibration and generating a calibration request; and adisplay operably coupled to the processing unit, the display beingconfigured to show a detection condition for the same substance inaccordance with the detection signal.
 2. The monitoring device of claim1, wherein the processing unit is configured to determine a displayreading based on a respective concentration detected by each of theplurality of sensors.
 3. The monitoring device of claim 1, wherein thecalibration action comprises self-calibration by adjusting a gain of asensor to minimize variance among the sensors for the substance.
 4. Themonitoring device of claim 1, further comprising a user interfaceconfigured to provide control signals to said processing unit, the userinterface comprising at least one of a button, key, or touch screen. 5.The monitoring device of claim 1, wherein the calibration actioncomprises self-calibration by adjusting a gain of a sensor according tothe relationship:${sensorGain}^{new} = {{sensorGain}^{old} + {0.1*\frac{\max \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)}{\min \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)}}}$6. The monitoring device of claim 1, wherein the processing unit isconfigured to determine a difference between signals from at least aportion of the plurality of sensors and to generate a sensor fail signalresponsive to the difference being outside of a threshold amount.
 7. Amethod for monitoring a substance using a monitoring device operablycoupled with a plurality of sensors, wherein each of the plurality ofsensors is configured to detect the same substance, the methodcomprising the steps of, by a processor: detecting a concentration ofthe substance based on output signals from each of the at least twosensors comprising concentration information associated with detectionof the same substance; determining a detection signal for said samesubstance based on the output signals; calculating a display reading ofthe substance, the display reading being determined based on anaggregate of the output signals; generating a calibration actionresponsive to the output signals deviating by a threshold amount, thecalibration action comprising at least one of performingself-calibration and generating a calibration request; comparing thedisplay reading to a threshold limit; and actuating an alarm in responseto the display reading deviating from the threshold limit by apredetermined value.
 8. The method of claim 7, further comprisingdetermining a gain of a majority of sensors, wherein the calibrationaction comprises self-calibration by adjusting a gain of a deviatingsensor to correspond with the gain of the majority of sensors.
 9. Themethod of claim 7, wherein the detection signal is determined based atleast partially on at least one of historic sensor data, span reserve ofthe respective sensors, gain of the respective sensors, or ambienttemperature.
 10. The method of claim 7, wherein the detection signal isdetermined according to the relationship:$R = \sqrt[k]{\frac{\sum\limits_{i = 0}^{n}R_{i}^{k}}{n}}$ wherein k isa value less than or equal to 1, n is a number of sensors that areindependently sensing the substance, R_(i) is a substance concentrationthat is detected by sensor i, and R is a substance concentrationdetermined by the processing unit.
 11. The method of claim 7, furthercomprising generating an aggregate substance concentration according tothe relationship:${R = \frac{\sum\limits_{i = 1}^{n}{w_{i}*R_{i}}}{n}},$ wherein w_(i)is a value between 0 and 1 representing a weight of sensor i, R is theaggregate substance concentration, R_(i) is a substance concentrationreading for sensor i, and n is a number of sensors.
 12. The method ofclaim 11, wherein w_(i) is proportional to a span reserve of sensor i.13. The method of claim 11, wherein w_(i) is proportional to a gain ofsensor i.
 14. A monitoring device for monitoring substances, themonitoring device comprising: at least two sensors configured to detecta same substance and to generate an output signal in response to adetection of the same substance; and a processing unit operably coupledto the at least two sensors, the processing unit being configured to:receive each of the output signals from the at least two sensors,determine a weight of each of the at least two sensors configured toindicate a reliability of each of the at least two sensors, anddetermine an aggregate substance concentration reading by aggregatingthe output signals from the at least two sensors biased toward outputsignals from sensors indicated as being more reliable based on theweights.
 15. The monitoring device of claim 14, wherein the weight isdetermined based on at least one of gain, span reserve, historiccalibration performance, and historic bump test performance.
 16. Themonitoring device of claim 14, further comprising a display operablycoupled to the processing unit, the display being configured to show adetection condition for the same substance in accordance with thedetection signal.
 17. The monitoring device of claim 14, wherein theprocessing unit is further configured to generate a calibration actionresponsive to at least two of the output signals deviating by athreshold amount, the calibration action comprising at least one ofperforming self-calibration and generating a calibration request. 18.The monitoring device of claim 17, wherein the calibration actioncomprises self-calibration by adjusting a gain of a sensor to minimizevariance among the sensors for the substance.
 19. The monitoring deviceof claim 17, further comprising a user interface configured to providecontrol signals to said processing unit, the user interface comprisingat least one of a button, key, or touch screen
 20. The monitoring deviceof claim 17, wherein the calibration action comprises self-calibrationby adjusting a gain of a sensor according to the relationship:${sensorGain}^{new} = {{sensorGain}^{old} + {0.1*{\frac{\max \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)}{\min \left( {{{sensorReading}\; 1},{{sensorReading}\; 2}} \right)}.}}}$